Abstract
While requirements focus on how the user interacts with the system, user stories concentrate on the purpose of software features. But in practice, functional requirements are also described in user stories. For this reason, requirements clarification is needed, especially when they are written in natural language and do not stick to any templates (e.g., “as an X, I want Y so that Z ...”). However, there is a lot of implicit knowledge that is not expressed in words. As a result, natural language requirements descriptions may suffer from incompleteness. Existing approaches try to formalize natural language or focus only on entirely missing and not on deficient requirements. In this paper, we therefore present an approach to detect knowledge gaps in user-generated software requirements for interactive requirement clarification: We provide tailored suggestions to the users in order to get more precise descriptions. For this purpose, we identify not fully instantiated predicate argument structures in requirements written in natural language and use context information to realize what was meant by the user.
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